## `summarise()` has grouped output by 'Dataset'. You can override using the
## `.groups` argument.
| Dataset | year | total_employee_debt |
|---|---|---|
| City | 2020 | 347611176292 |
| City | 2021 | 321349270209 |
| City | 2022 | 292943121949 |
| County | 2020 | 212341520461 |
| County | 2021 | 215283148045 |
| County | 2022 | 169588944680 |
| SD | 2020 | 390796402705 |
| SD | 2021 | 426683530903 |
| SD | 2022 | 315437044335 |
| State | 2020 | 1235541058529 |
| State | 2021 | 1346791618238 |
| State | 2022 | 1077609632518 |
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
Partisan lean explains about 28.13% of the variability in debt_to_GDP_ratio (**)
##
## Call:
## lm(formula = debt_to_GDP_ratio ~ log(lean), data = State3y_debtGDP_ratio_2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.095 -5.577 -2.404 3.025 14.831
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.452 4.080 1.336 0.1991
## log(lean) 4.101 1.590 2.580 0.0195 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.092 on 17 degrees of freedom
## Multiple R-squared: 0.2813, Adjusted R-squared: 0.2391
## F-statistic: 6.655 on 1 and 17 DF, p-value: 0.01948
Population explains 14.98% of the variability in the net pension liability (***)
##
## Call:
## lm(formula = net_pension_liability ~ log(population), data = State3y_debtGDP_ratio_2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -44309311004 -17563340509 -2003468249 4466882069 102015454800
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -213465931342 104184854027 -2.049 0.0562 .
## log(population) 15354874348 6819389377 2.252 0.0379 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 32280000000 on 17 degrees of freedom
## Multiple R-squared: 0.2297, Adjusted R-squared: 0.1844
## F-statistic: 5.07 on 1 and 17 DF, p-value: 0.03786
Population explains about 31.21% of the variability in the net OPEB liability (***)
##
## Call:
## lm(formula = net_opeb_liability ~ log(population), data = State3y_debtGDP_ratio_2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -32984911853 -17608027121 3596809542 11233737075 53399071062
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -272048168891 73153015707 -3.719 0.001706 **
## log(population) 19165852594 4788209408 4.003 0.000922 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 22660000000 on 17 degrees of freedom
## Multiple R-squared: 0.4852, Adjusted R-squared: 0.4549
## F-statistic: 16.02 on 1 and 17 DF, p-value: 0.0009218
There is no significant relationship between partisan lean and net pension liability.
##
## Call:
## lm(formula = lean ~ log(net_pension_liability), data = State3y_debtGDP_ratio_2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -11.371 -8.317 -3.416 9.233 16.463
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -23.042 30.733 -0.750 0.464
## log(net_pension_liability) 1.701 1.369 1.243 0.231
##
## Residual standard error: 10.13 on 17 degrees of freedom
## Multiple R-squared: 0.08331, Adjusted R-squared: 0.02939
## F-statistic: 1.545 on 1 and 17 DF, p-value: 0.2307
there is no significant relationship between net OPEB liability and partisan lean
##
## Call:
## lm(formula = lean ~ log(net_opeb_liability), data = State3y_debtGDP_ratio_2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.932 -7.522 -3.084 7.511 16.599
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -23.828 26.581 -0.896 0.383
## log(net_opeb_liability) 1.742 1.187 1.468 0.160
##
## Residual standard error: 9.963 on 17 degrees of freedom
## Multiple R-squared: 0.1125, Adjusted R-squared: 0.06031
## F-statistic: 2.155 on 1 and 17 DF, p-value: 0.1603
There is no significant relationship between partisan lean and employee debt per capita.
##
## Call:
## lm(formula = employee_debt_per_capita ~ log(lean), data = State3y_debtGDP_ratio_2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4993 -3372 -2060 1161 11851
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1562 2993 0.522 0.608
## log(lean) 1743 1166 1.495 0.153
##
## Residual standard error: 5203 on 17 degrees of freedom
## Multiple R-squared: 0.1162, Adjusted R-squared: 0.06417
## F-statistic: 2.234 on 1 and 17 DF, p-value: 0.1533
Moves in Unfunded Liabilities from 2020-2021
| Dataset | year | TotalOPEB | TotalNPL |
|---|---|---|---|
| City | 2020 | $156,522,239,133 | $178,384,996,141 |
| City | 2021 | $165,964,671,391 | $141,260,147,751 |
| City | 2022 | $130,905,745,444 | $146,684,931,141 |
| County | 2020 | $88,087,255,809 | $114,215,472,120 |
| County | 2021 | $92,040,044,677 | $111,117,569,436 |
| County | 2022 | $81,023,510,007 | $77,250,886,492 |
| SD | 2020 | $306,910,533,728 | $234,103,265,490 |
| SD | 2021 | $334,204,539,712 | $260,126,175,028 |
| SD | 2022 | $284,753,087,046 | $219,188,455,066 |
| State | 2020 | $524,908,977,684 | $684,637,170,415 |
| State | 2021 | $574,806,860,928 | $743,183,954,659 |
| State | 2022 | $528,409,889,023 | $520,514,394,453 |
| Entity | NPL Change | NPL Percent Change | Net OPEB Change | OPEB Percent Change |
|---|---|---|---|---|
| State | -$164,122,775,962 | -24.0% | $3,500,911,339 | 0.67% |
| SD | -$14,914,810,424 | -6.4% | -$22,157,446,682 | -7.22% |
| City | -$31,700,065,000 | -17.8% | -$25,616,493,689 | -16.37% |
| County | -$36,964,585,628 | -32.4% | -$7,063,745,802 | -8.02% |
Moves in Unfunded Liabiities from 2020-2021
## `geom_smooth()` using formula = 'y ~ x'
States: Correlation Between % Difference in NPL Population
## `geom_smooth()` using formula = 'y ~ x'
Cities: Correlation Between % Difference in NPL Population
## `geom_smooth()` using formula = 'y ~ x'
Counties: Correlation Between % Difference in NPL Population
## `geom_smooth()` using formula = 'y ~ x'
School Districts: Correlation Between % Difference in NPL Population
Total Unfunded Liabilities 2020-2022 - Cities divided by Population Quartiles
| Quartile | Min | Max |
|---|---|---|
| 1 | 208,640 | 259,520 |
| 2 | 263,914 | 383,998 |
| 3 | 384,661 | 631,539 |
| 4 | 639,115 | 8,804,194 |
| Year | Pop Quartile | Total NPL | % Change from Previous Year | $ Change from Previous Year |
|---|---|---|---|---|
| 2021 | 1 | $5,228,004,831 | -3% | -$137,372,891 |
| 2022 | 1 | $3,224,089,824 | -38% | -$2,003,915,007 |
| 2021 | 2 | $9,216,231,780 | -14% | -$1,553,009,578 |
| 2022 | 2 | $6,740,056,661 | -27% | -$2,476,175,119 |
| 2021 | 3 | $18,077,273,298 | 0% | -$7,913,508 |
| 2022 | 3 | $13,913,327,591 | -23% | -$4,163,945,707 |
| 2021 | 4 | $108,738,637,842 | -25% | -$35,426,552,413 |
| 2022 | 4 | $122,807,457,065 | 13% | $14,068,819,223 |
melted_corStates <- melt(corSD)
ggplot(melted_corStates, aes(Var1, Var2, fill = value)) + geom_tile()
+
geom_text(aes(label = sprintf(“%.2f”, value)), color = “black”, size =
3) +
scale_fill_gradient2(low = “red”, high = “green”, mid = “gray”, midpoint
= 0) +
theme_minimal() +
theme( axis.text.x = element_text(angle = 45, hjust = 1), axis.text.y =
element_text(angle = 45, hjust = 1)
) + labs( title = “Correlation Matrix: School Districts”,
x = ““,
y =”“,
fill =”Correlation Strength” )
| Entity | Year | Net OPEB Change | OPEB Percent Change |
|---|---|---|---|
| State | 2022 | $3,500,911,339 | 0.67% |
| SD | 2022 | -$22,157,446,682 | -7.22% |
| City | 2022 | -$25,616,493,689 | -16.37% |
| County | 2022 | -$7,063,745,802 | -8.02% |
| Year | Net OPEB Change | OPEB Percent Change |
|---|---|---|
| 2020 | $3,500,911,339 | 1% |
| 2021 | $3,500,911,339 | 1% |
| 2022 | $3,500,911,339 | 1% |
| 2020 | $684,637,170,415.00 | NA |
| 2021 | $743,183,954,659.00 | 8.55% |
| 2022 | $520,514,394,453.00 | -29.96% |
| State | Net Pension Liability | Per Capita |
|---|---|---|
| Illinois | $139,846,404,000 | $10,914.80 |
| New Jersey | $75,075,280,124 | $8,082.14 |
| California | $54,169,128,000 | $1,370.04 |
| Connecticut | $36,132,877,000 | $10,020.37 |
| Massachusetts | $34,806,645,000 | $4,951.19 |
| Texas | $30,883,865,000 | $1,059.65 |
| Kentucky | $25,034,578,000 | $5,555.96 |
| Maryland | $13,366,859,000 | $2,163.90 |
| Pennsylvania | $12,550,399,000 | $965.22 |
| Indiana | $9,781,677,000 | $1,441.52 |
| State | Per Capita NPL | Total NPL |
|---|---|---|
| Illinois | $10,914.80 | $139,846,404,000 |
| Connecticut | $10,020.37 | $36,132,877,000 |
| New Jersey | $8,082.14 | $75,075,280,124 |
| Kentucky | $5,555.96 | $25,034,578,000 |
| Massachusetts | $4,951.19 | $34,806,645,000 |
| Hawaii | $4,345.08 | $6,323,283,000 |
| Vermont | $3,906.27 | $2,512,066,064 |
| Alaska | $3,253.06 | $2,385,726,000 |
| Rhode Island | $2,585.82 | $2,837,608,000 |
| New Mexico | $2,373.82 | $5,026,620,000 |
| 2020 | $178,384,996,141.00 | NA |
| 2021 | $141,260,147,751.00 | -20.81% |
| 2022 | $146,684,931,141.00 | 3.84% |
| City | State | Net Pension Liability | Per Capita |
|---|---|---|---|
| new york | NY | $42,349,466,000 | $4,810.15 |
| chicago | IL | $35,436,606,000 | $12,903.16 |
| philadelphia | PA | $5,386,419,000 | $3,358.54 |
| phoenix | AZ | $4,643,155,000 | $2,887.19 |
| los angeles | CA | $4,363,649,000 | $1,119.24 |
| portland | OR | $4,275,718,773 | $6,552.64 |
| dallas | TX | $4,029,247,000 | $3,089.16 |
| jacksonville | FL | $2,905,810,000 | $3,059.98 |
| san jose | CA | $2,342,879,000 | $2,312.31 |
| houston | TX | $2,322,472,000 | $1,009.08 |
| City | State | Per Capita NPL | Total NPL |
|---|---|---|---|
| chicago | IL | $12,903.16 | $35,436,606,000 |
| portland | OR | $6,552.64 | $4,275,718,773 |
| new york | NY | $4,810.15 | $42,349,466,000 |
| philadelphia | PA | $3,358.54 | $5,386,419,000 |
| dallas | TX | $3,089.16 | $4,029,247,000 |
| cincinnati | OH | $3,086.75 | $955,536,000 |
| jacksonville | FL | $3,059.98 | $2,905,810,000 |
| pittsburgh | PA | $3,050.90 | $924,293,454 |
| miami | FL | $2,942.66 | $1,301,418,710 |
| phoenix | AZ | $2,887.19 | $4,643,155,000 |
| 2020 | $114,215,472,120.00 | NA |
| 2021 | $111,117,569,436.00 | -2.71% |
| 2022 | $77,250,886,492.00 | -30.48% |
| County | State | Net Pension Liability | Per Capita |
|---|---|---|---|
| cook county | IL | $10,837,262,892 | $2,054.25 |
| los angeles county | CA | $7,030,463,000 | $702.06 |
| philadelphia | PA | $5,386,419,000 | $3,358.54 |
| miami-dade county | FL | $3,979,267,000 | $1,472.84 |
| santa clara county | CA | $2,989,893,000 | $1,544.15 |
| jacksonville | FL | $2,905,810,000 | $2,918.77 |
| city and county of honolulu | HI | $2,270,106,000 | $2,233.24 |
| san diego county | CA | $2,246,673,000 | $681.09 |
| orange county | CA | $2,048,680,000 | $642.83 |
| prince georges county | MD | $2,011,969,036 | $2,080.21 |
| County | State | Per Capita | Total NPL |
|---|---|---|---|
| philadelphia | PA | $3,358.54 | $5,386,419,000 |
| jacksonville | FL | $2,918.77 | $2,905,810,000 |
| city and county of honolulu | HI | $2,233.24 | $2,270,106,000 |
| prince georges county | MD | $2,080.21 | $2,011,969,036 |
| cook county | IL | $2,054.25 | $10,837,262,892 |
| denver county | CO | $2,032.63 | $1,454,425,000 |
| baltimore county | MD | $1,863.20 | $1,592,201,000 |
| santa clara county | CA | $1,544.15 | $2,989,893,000 |
| kern county | CA | $1,541.70 | $1,401,786,000 |
| dekalb county | GA | $1,531.02 | $1,170,291,000 |
| 2020 | $234,103,265,490.00 | NA |
| 2021 | $260,126,175,028.00 | 11.12% |
| 2022 | $219,188,455,065.70 | -15.74% |
| County | State | Net Pension Liability | Per Student |
|---|---|---|---|
| Chicago Board of Education | IL | $14,727,410,000 | $44,650.71 |
| Chicago Board of Education | IL | $14,727,410,000 | $44,650.71 |
| Chicago Board of Education | IL | $14,727,410,000 | $44,650.71 |
| Los Angeles Unified School District | CA | $4,311,675,000 | $9,890.12 |
| Los Angeles Unified School District | CA | $4,311,675,000 | $9,890.12 |
| Los Angeles Unified School District | CA | $4,311,675,000 | $9,890.12 |
| School District of Philadelphia | PA | $3,253,100,000 | $27,556.27 |
| School District of Philadelphia | PA | $3,253,100,000 | $27,556.27 |
| School District of Philadelphia | PA | $3,253,100,000 | $27,556.27 |
| Clark County School District | NV | $2,167,406,738 | $6,863.51 |
| County | State | Per Student Net Pension Liability | Total NPL |
|---|---|---|---|
| Chicago Board of Education | IL | $44,650.71 | $14,727,410,000 |
| Chicago Board of Education | IL | $44,650.71 | $14,727,410,000 |
| Chicago Board of Education | IL | $44,650.71 | $14,727,410,000 |
| School District of Philadelphia | PA | $27,556.27 | $3,253,100,000 |
| School District of Philadelphia | PA | $27,556.27 | $3,253,100,000 |
| School District of Philadelphia | PA | $27,556.27 | $3,253,100,000 |
| NEW YORK CITY GEOGRAPHIC DISTRICT # 2 | NY | $16,481.75 | $937,696,211 |
| NEW YORK CITY GEOGRAPHIC DISTRICT #10 | NY | $16,481.75 | $937,696,211 |
| NEW YORK CITY GEOGRAPHIC DISTRICT #20 | NY | $16,481.75 | $937,696,211 |
| NEW YORK CITY GEOGRAPHIC DISTRICT #24 | NY | $16,481.75 | $937,696,211 |